Estimation by maximum likelihood of the multinomial logit model, with alternative-specific and/or individual specific variables.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ```
mlogit(formula, data, subset, weights, na.action, start = NULL,
alt.subset = NULL, reflevel = NULL,
nests = NULL, un.nest.el = FALSE, unscaled = FALSE,
heterosc = FALSE, rpar = NULL, probit = FALSE,
R = 40, correlation = FALSE, halton = NULL,
random.nb = NULL, panel = FALSE, estimate = TRUE,
seed = 10, ...)
## S3 method for class 'mlogit'
print(x, digits = max(3, getOption("digits") - 2),
width = getOption("width"), ...)
## S3 method for class 'mlogit'
summary(object, ...)
## S3 method for class 'summary.mlogit'
print(x, digits = max(3, getOption("digits") - 2),
width = getOption("width"), ...)
## S3 method for class 'mlogit'
print(x, digits = max(3, getOption("digits") - 2),
width = getOption("width"), ...)
## S3 method for class 'mlogit'
logLik(object, ...)
## S3 method for class 'mlogit'
residuals(object, outcome = TRUE, ...)
## S3 method for class 'mlogit'
fitted(object, outcome = TRUE, ...)
## S3 method for class 'mlogit'
predict(object, newdata, returnData = FALSE, ...)
## S3 method for class 'mlogit'
df.residual(object, ...)
## S3 method for class 'mlogit'
terms(x, ...)
## S3 method for class 'mlogit'
model.matrix(object, ...)
## S3 method for class 'mlogit'
update(object, new, ...)
``` |

`x, object` |
an object of class |

`formula` |
a symbolic description of the model to be estimated, |

`new` |
an updated formula for the |

`newdata` |
a |

`returnData` |
if |

`data` |
the data: an |

`subset` |
an optional vector specifying a subset of observations, |

`weights` |
an optional vector of weights, |

`na.action` |
a function which indicates what should happen when
the data contains ' |

`start` |
a vector of starting values, |

`alt.subset` |
a vector of character strings containing the subset of alternative on which the model should be estimated, |

`reflevel` |
the base alternative (the one for which the coefficients of individual-specific variables are normalized to 0), |

`nests` |
a named list of characters vectors, each names being a nest, the corresponding vector being the set of alternatives that belong to this nest, |

`un.nest.el` |
a boolean, if |

`unscaled` |
a boolean, if |

`heterosc` |
a boolean, if |

`rpar` |
a named vector whose names are the random parameters and
values the distribution : |

`probit` |
if |

`R` |
the number of function evaluation for the gaussian quadrature
method used if |

`correlation` |
only relevant if |

`halton` |
only relevant if |

`random.nb` |
only relevant if |

`panel` |
only relevant if |

`estimate` |
a boolean indicating whether the model should be
estimated or not: if not, the |

`seed` |
, |

`digits` |
the number of digits, |

`width` |
the width of the printing, |

`outcome` |
a boolean which indicates, for the |

`...` |
further arguments passed to |

For how to use the formula argument, see `mFormula`

.

The `data`

argument may be an ordinary `data.frame`

. In this
case, some supplementary arguments should be provided and are passed
to `mlogit.data`

. Note that it is not necessary to indicate the
choice argument as it is deduced from the formula.

The model is estimated using the `mlogit.optim`

function.

The basic multinomial logit model and three important extentions of this model may be estimated.

If `heterosc=TRUE`

, the heteroscedastic logit model is
estimated. `J-1`

extra coefficients are estimated that represent
the scale parameter for `J-1`

alternatives, the scale parameter
for the reference alternative being normalized to 1. The probabilities
don't have a closed form, they are estimated using a gaussian
quadrature method.

If `nests`

is not `NULL`

, the nested logit model is
estimated.

If `rpar`

is not `NULL`

, the random parameter model is
estimated. The probabilities are approximated using simulations with
`R`

draws and halton sequences are used if `halton`

is not
`NULL`

. Pseudo-random numbers are drawns from a standard normal
and the relevant transformations are performed to obtain numbers
drawns from a normal, log-normal, censored-normal or uniform
distribution. If `correlation=TRUE`

, the correlation between the
random parameters are taken into account by estimating the components
of the cholesky decomposition of the covariance matrix. With G random
parameters, without correlation G standard deviations are estimated,
with correlation G * (G + 1) /2 coefficients are estimated.

An object of class `"mlogit"`

, a list with elements:

`coefficients` |
the named vector of coefficients, |

`logLik` |
the value of the log-likelihood, |

`hessian` |
the hessian of the log-likelihood at convergence, |

`gradient` |
the gradient of the log-likelihood at convergence, |

`call` |
the matched call, |

`est.stat` |
some information about the estimation (time used, optimisation method), |

`freq` |
the frequency of choice, |

`residuals` |
the residuals, |

`fitted.values` |
the fitted values, |

`formula` |
the formula (a |

`expanded.formula` |
the formula (a |

`model` |
the model frame used, |

`index` |
the index of the choice and of the alternatives. |

Yves Croissant

McFadden, D. (1973) Conditional Logit Analysis of Qualitative Choice
Behavior, in P. Zarembka ed., *Frontiers in Econometrics*,
New-York: Academic Press.

McFadden, D. (1974) “The Measurement of Urban Travel Demand”,
*Journal of Public Economics*, 3, pp. 303-328.

Train, K. (2004) *Discrete Choice Modelling, with Simulations*,
Cambridge University Press.

`mlogit.data`

to shape the data. `multinom`

from
package `nnet`

performs the estimation of the multinomial logit
model with individual specific variables. `mlogit.optim`

for details about the optimization function.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | ```
## Cameron and Trivedi's Microeconometrics p.493 There are two
## alternative specific variables : price and catch one individual
## specific variable (income) and four fishing mode : beach, pier, boat,
## charter
data("Fishing", package = "mlogit")
Fish <- mlogit.data(Fishing, varying = c(2:9), shape = "wide", choice = "mode")
## a pure "conditional" model
summary(mlogit(mode ~ price + catch, data = Fish))
## a pure "multinomial model"
summary(mlogit(mode ~ 0 | income, data = Fish))
## which can also be estimated using multinom (package nnet)
library("nnet")
summary(multinom(mode ~ income, data = Fishing))
## a "mixed" model
m <- mlogit(mode ~ price+ catch | income, data = Fish)
summary(m)
## same model with charter as the reference level
m <- mlogit(mode ~ price+ catch | income, data = Fish, reflevel = "charter")
## same model with a subset of alternatives : charter, pier, beach
m <- mlogit(mode ~ price+ catch | income, data = Fish,
alt.subset = c("charter", "pier", "beach"))
## model on unbalanced data i.e. for some observations, some
## alternatives are missing
# a data.frame in wide format with two missing prices
Fishing2 <- Fishing
Fishing2[1, "price.pier"] <- Fishing2[3, "price.beach"] <- NA
mlogit(mode~price+catch|income, Fishing2, shape="wide", choice="mode", varying = 2:9)
# a data.frame in long format with three missing lines
data("TravelMode", package = "AER")
Tr2 <- TravelMode[-c(2, 7, 9),]
mlogit(choice~wait+gcost|income+size, Tr2, shape = "long",
chid.var = "individual", alt.var="mode", choice = "choice")
## An heteroscedastic logit model
data("TravelMode", package = "AER")
hl <- mlogit(choice ~ wait + travel + vcost, TravelMode,
shape = "long", chid.var = "individual", alt.var = "mode",
method = "bfgs", heterosc = TRUE, tol = 10)
## A nested logit model
TravelMode$avincome <- with(TravelMode, income * (mode == "air"))
TravelMode$time <- with(TravelMode, travel + wait)/60
TravelMode$timeair <- with(TravelMode, time * I(mode == "air"))
TravelMode$income <- with(TravelMode, income / 10)
# Hensher and Greene (2002), table 1 p.8-9 model 5
TravelMode$incomeother <- with(TravelMode, ifelse(mode %in% c('air', 'car'), income, 0))
nl <- mlogit(choice~gcost+wait+incomeother, TravelMode,
shape='long', alt.var='mode',
nests=list(public=c('train', 'bus'), other=c('car','air')))
# same with a comon nest elasticity (model 1)
nl2 <- update(nl, un.nest.el = TRUE)
## a probit model
## Not run:
pr <- mlogit(choice ~ wait + travel + vcost, TravelMode,
shape = "long", chid.var = "individual", alt.var = "mode",
probit = TRUE)
## End(Not run)
## a mixed logit model
## Not run:
rpl <- mlogit(mode ~ price+ catch | income, Fishing, varying = 2:9,
shape = 'wide', rpar = c(price= 'n', catch = 'n'),
correlation = TRUE, halton = NA,
R = 10, tol = 10, print.level = 0)
summary(rpl)
rpar(rpl)
cor.mlogit(rpl)
cov.mlogit(rpl)
rpar(rpl, "catch")
summary(rpar(rpl, "catch"))
## End(Not run)
# a ranked ordered model
data("Game", package = "mlogit")
g <- mlogit(ch~own|hours, Game, choice='ch', varying = 1:12,
ranked=TRUE, shape="wide", reflevel="PC")
``` |

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